The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Layout-aware Subfigure Decomposition for Complex Figures in the Biomedical Literature
Abstract
Published scientific figure is a valuable information resource, but often occur as composite images. The ImageCLEF meeting presented a shared evaluation in 2016 to use machine learning to split these composite figures into components automatically. We adapted an existing high-performance object detection method to analyze the substructure of published biomedical figures by developing a novel multi-branch output convolution neural network to predict irregular panel layouts and provide augmented training data to drive learning. Our system has an accuracy of 86.8% on the 2016 ImageCLEF Medical dataset and 83.1% on a new dataset derived from open access papers from the INTACT database of molecular interactions.
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Figures in biomedical documents are compound figures comprising biomedical images, charts, tables and other artifacts.
“Furthermore, figures in biomedical documents are compound figures [43, 47] i.e. a figure comprises biomedical images, charts, tables and other artifacts.”
From BioFors: A Large Biomedical Image Forensics Dataset (2021) · cites this paper as [43]
State-of-the-art biomedical figure decomposition models have imperfect and overlapping crop boundaries.
“Sadly, stateof-the-art biomedical figure decomposition models [43, 47] have imperfect and overlapping crop boundaries.”
From BioFors: A Large Biomedical Image Forensics Dataset (2021) · cites this paper as [43]
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks
Tsutsui, S., & Crandall, D. J. (2017) · 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2017.93 · Cited by 40
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Research works printed but not linked: 17
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BioFors: A Large Biomedical Image Forensics Dataset
Sabir, E., Nandi, S., AbdAlmageed, W., & Natarajan, P. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01078 · Cited by 12
“Sadly, stateof-the-art biomedical figure decomposition models [43, 47] have imperfect and overlapping crop boundaries.”
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Metadata
- Authors
- Shi, X., Wu, Y., Cao, H., Burns, G., & Natarajan, P.
- Affiliations
- Information Sciences Institute, Marina del Rey, California, U.S.A.
- Venue
- ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 1343-1347
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2937176418
- Cited by
- 14
- References
- 4
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 2 · 2021: 6 · 2020: 1 · 2019: 1
- FWCI
- 0.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- multi-branch convolutional neural network, object detection, data augmentation
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